Machine Learning in Predicting Drug-Drug Interactions: Enhancing Patient Safety
Aadarsh joshi
, Anamika pant
Key Words: Drug-Drug Interactions (DDIs), Machine Learning (ML), Patient Safety, Adverse Drug Reactions, Deep Learning, Ensemble Methods, Natural Language Processing (NLP), Biomedical Data, Electronic Health Records (EHRs), Pharmacovigilance, Personalized Medicine, Predictive Modeling, Healthcare Optimization.
In clinical exercise and drug improvement, the prediction of drug-drug interactions (DDIs) is a crucial component of patient protection. The human curation of statistics and clinical observations utilized in conventional methods of figuring out DDIs may be exhausting and prone to blunders. The method of forecasting DDIs has been substantially advanced with the creation of system getting to know (ML), imparting extra precise, powerful, and scalable answers. using device getting to know algorithms to predict DDIs is tested in this paintings, with a specific emphasis on deep gaining knowledge of, ensemble techniques, and natural language processing (NLP). ML models can as it should be stumble on feasible DDIs by way of utilizing a plethora of biomedical records, which includes remedy traits, molecular interactions, and patient information. The prediction strength of these fashions is further greater by the incorporation of real records from pharma covigilance databases and electronic fitness information (EHRs). by lowering the opportunity of adverse drug reactions, using system mastering (ML) to anticipate drug-drug interactions (DDIs) improves patient protection and facilitates optimize drug remedy regimens, which in turn cause extra individualized and green healthcare. The problems with ML-based totally DDI prediction are also included on this observe, alongside feasible answers to those troubles. Those problems encompass data satisfactory, model interpretability, and the requirement for reliable validation techniques. The outcomes highlight how gadget getting to know may be modern in defensive affected person safety and enhancing pharmaceutical studies.
"Machine Learning in Predicting Drug-Drug Interactions: Enhancing Patient Safety", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 9, page no.272 - 275, September-2024, Available :https://ijsdr.org/papers/IJSDR2409029.pdf
Volume 9
Issue 9,
September-2024
Pages : 272 - 275
Paper Reg. ID: IJSDR_212464
Published Paper Id: IJSDR2409029
Downloads: 000347063
Research Area: Computer Science & Technology
Country: Bilaspur, Uttar Pradesh, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
